Experimental Efficient Influence Sampling of Quantum Processes
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915810349416448 |
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| author | Zhan, Hao Bao, Zongbo Ye, Zekun Wang, Qianyi Mi, Minghao Yao, Penghui Zhang, Lijian |
| author_facet | Zhan, Hao Bao, Zongbo Ye, Zekun Wang, Qianyi Mi, Minghao Yao, Penghui Zhang, Lijian |
| contents | Characterizing quantum processes is essential for unlocking the potential of quantum devices. However, standard quantum process tomography is resource-intensive and becomes infeasible on large-scale systems. Despite alternative approaches have been successfully developed for specific scenarios, they typically rely on multi-qubit gates or extensive prior knowledge, limiting their practicability and scalability. To address these challenges and complement existing approaches, we introduce $\textit{influence sampling}$, an efficient and scalable protocol that quantifies the $\textit{influence}$ of a quantum process on all qubit subsets using only single-qubit test gates, with sample complexity independent of system size. Using a photonic platform, we demonstrate influence sampling to identify high-influence qubits, reduce the full process to a smaller effective process, i.e., a junta approximation, and then learn it. We further confirm scalability by applying the protocol to a 24-qubit system and validate the junta approximation on a two-qubit process. These results establish influence sampling as a critical characterization technique, facilitating process learning and device assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental Efficient Influence Sampling of Quantum Processes Zhan, Hao Bao, Zongbo Ye, Zekun Wang, Qianyi Mi, Minghao Yao, Penghui Zhang, Lijian Quantum Physics Characterizing quantum processes is essential for unlocking the potential of quantum devices. However, standard quantum process tomography is resource-intensive and becomes infeasible on large-scale systems. Despite alternative approaches have been successfully developed for specific scenarios, they typically rely on multi-qubit gates or extensive prior knowledge, limiting their practicability and scalability. To address these challenges and complement existing approaches, we introduce $\textit{influence sampling}$, an efficient and scalable protocol that quantifies the $\textit{influence}$ of a quantum process on all qubit subsets using only single-qubit test gates, with sample complexity independent of system size. Using a photonic platform, we demonstrate influence sampling to identify high-influence qubits, reduce the full process to a smaller effective process, i.e., a junta approximation, and then learn it. We further confirm scalability by applying the protocol to a 24-qubit system and validate the junta approximation on a two-qubit process. These results establish influence sampling as a critical characterization technique, facilitating process learning and device assessment. |
| title | Experimental Efficient Influence Sampling of Quantum Processes |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.07103 |